Title :
Support vector tracking
Author_Institution :
MobilEye Vision Technol. LTD, Jerusalem, Israel
Abstract :
Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.
Keywords :
image classification; image sequences; minimisation; support vector machines; vehicles; SVM classification; coarse to fine approach; image sequences; intensity difference function; maximization; minimization; optic flow based tracker; successive frames; support vector machine classifier; support vector tracking; vehicle tracking; Brightness; Cameras; Image sequences; Integrated optics; Optical filters; Optical sensors; Support vector machine classification; Support vector machines; Vehicle detection; Video sequences; Support vector machines; optic-flow; visual tracking.; Algorithms; Artificial Intelligence; Computer Graphics; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Movement; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Video Recording;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2004.53
